| Accelerating high-throughput virtual screening through molecular pool-based active learning DE Graff, EI Shakhnovich, CW Coley Chemical science 12 (22), 7866-7881, 2021 | 245 | 2021 |
| Chemprop: a machine learning package for chemical property prediction E Heid, KP Greenman, Y Chung, SC Li, DE Graff, FH Vermeire, H Wu, ... Journal of Chemical Information and Modeling 64 (1), 9-17, 2023 | 190 | 2023 |
| Intermolecular anti-Markovnikov hydroamination of unactivated alkenes with sulfonamides enabled by proton-coupled electron transfer Q Zhu, DE Graff, RR Knowles Journal of the American Chemical Society 140 (2), 741-747, 2018 | 188 | 2018 |
| Enantioselective hydroamination of alkenes with sulfonamides enabled by proton-coupled electron transfer CB Roos, J Demaerel, DE Graff, RR Knowles Journal of the American Chemical Society 142 (13), 5974-5979, 2020 | 120 | 2020 |
| Self-focusing virtual screening with active design space pruning DE Graff, M Aldeghi, JA Morrone, KE Jordan, EO Pyzer-Knapp, CW Coley Journal of Chemical Information and Modeling 62 (16), 3854-3862, 2022 | 35 | 2022 |
| Roughness of molecular property landscapes and its impact on modellability M Aldeghi, DE Graff, N Frey, JA Morrone, EO Pyzer-Knapp, KE Jordan, ... Journal of Chemical Information and Modeling 62 (19), 4660-4671, 2022 | 34 | 2022 |
| Accelerating High-Throughput Virtual Screening through Molecular Pool-Based Active Learning, Chem DE Graff, EI Shakhnovich, CW Coley Sci 12 (22), 7866-7881, 2021 | 10 | 2021 |
| Pareto optimization to accelerate multi-objective virtual screening JC Fromer, DE Graff, CW Coley Digital Discovery 3 (3), 467-481, 2024 | 8 | 2024 |
| Pyscreener: a python wrapper for computational docking software DE Graff, CW Coley arXiv preprint arXiv:2112.10575, 2021 | 8 | 2021 |
| Evaluating the roughness of structure–property relationships using pretrained molecular representations DE Graff, EO Pyzer-Knapp, KE Jordan, EI Shakhnovich, CW Coley Digital Discovery 2 (5), 1452-1460, 2023 | 4 | 2023 |
| A physics-inspired approach to the understanding of molecular representations and models L Dicks, DE Graff, KE Jordan, CW Coley, EO Pyzer-Knapp Molecular Systems Design & Engineering 9 (5), 449-455, 2024 | 2 | 2024 |
| Challenging reaction prediction models to generalize to novel chemistry J Bradshaw, A Zhang, B Mahjour, DE Graff, MHS Segler, CW Coley arXiv preprint arXiv:2501.06669, 2025 | | 2025 |
| Identification of Antituberculars with Favorable Potency and Pharmacokinetics through Structure-Based and Ligand-Based Modeling V Waradpande, F Meng, A Bozan, DE Graff, JC Fromer, K Mughal, ... bioRxiv, 2025.02. 03.636334, 2025 | | 2025 |
| Chemprop: Property Prediction, Uncertainty Quantification, Software Development, and Benchmarking E Heid, KP Greenman, Y Chung, SC Li, DE Graff, FH Vermeire, H Wu, ... Optical Property Prediction and Molecular Discovery through Multi-Fidelity …, 2024 | | 2024 |
| Pareto Optimization to Accelerate Multi-Property Virtual Screening J Fromer, D Graff, C Coley 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Accelerating Discovery in Virtual Chemical Libraries DE Graff Harvard University, 2023 | | 2023 |
| Chemprop: Machine Learning for Molecular Property Prediction C McGill, E Heid, Y Chung, K Greenman, D Graff, M Liu, C Bilodeau, ... 2022 AIChE Annual Meeting, 2022 | | 2022 |
| A Tale of Two Hydroaminations: Development of Two Novel Reactions for Alkene Hydroamination Enabled by Proton-Coupled Electron Transfer D Graff | | 2018 |
| MolPAL: Software for Sample Efficient High-Throughput Virtual Screening DE Graff, CW Coley AI for Accelerated Materials Design NeurIPS 2022 Workshop, 0 | | |
Connor W. ColeyMassachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
